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<a href="#pub-types">Public 类型</a> &#124;
<a href="#pub-methods">Public 成员函数</a> &#124;
<a href="#pri-attribs">Private 属性</a> &#124;
<a href="classpcl_1_1_v_f_h_classifier_n_n-members.html">所有成员列表</a>  </div>
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<div class="title">pcl::VFHClassifierNN类 参考</div>  </div>
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<p>Utility class for nearest neighbor search based classification of VFH features.  
 <a href="classpcl_1_1_v_f_h_classifier_n_n.html#details">更多...</a></p>

<p><code>#include &lt;<a class="el" href="vfh__nn__classifier_8h_source.html">vfh_nn_classifier.h</a>&gt;</code></p>
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Public 类型</h2></td></tr>
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typedef <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_v_f_h_signature308.html">pcl::VFHSignature308</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>FeatureCloud</b></td></tr>
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typedef <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_v_f_h_signature308.html">pcl::VFHSignature308</a> &gt;::Ptr&#160;</td><td class="memItemRight" valign="bottom"><b>FeatureCloudPtr</b></td></tr>
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<tr class="memitem:a06ccc6fba71ef1cc134a6c11f43e1498"><td class="memItemLeft" align="right" valign="top"><a id="a06ccc6fba71ef1cc134a6c11f43e1498"></a>
typedef <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_v_f_h_signature308.html">pcl::VFHSignature308</a> &gt;::ConstPtr&#160;</td><td class="memItemRight" valign="bottom"><b>FeatureCloudConstPtr</b></td></tr>
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typedef <a class="el" href="classpcl_1_1_n_n_classification.html">NNClassification</a>&lt; <a class="el" href="structpcl_1_1_v_f_h_signature308.html">pcl::VFHSignature308</a> &gt;::Result&#160;</td><td class="memItemRight" valign="bottom"><b>Result</b></td></tr>
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typedef <a class="el" href="classpcl_1_1_n_n_classification.html">NNClassification</a>&lt; <a class="el" href="structpcl_1_1_v_f_h_signature308.html">pcl::VFHSignature308</a> &gt;::ResultPtr&#160;</td><td class="memItemRight" valign="bottom"><b>ResultPtr</b></td></tr>
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Public 成员函数</h2></td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>reset</b> ()</td></tr>
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<tr class="memitem:a17909e1d3ca700241cc7a5564d1b73fc"><td class="memItemLeft" align="right" valign="top"><a id="a17909e1d3ca700241cc7a5564d1b73fc"></a>
void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_v_f_h_classifier_n_n.html#a17909e1d3ca700241cc7a5564d1b73fc">finalizeTraining</a> ()</td></tr>
<tr class="memdesc:a17909e1d3ca700241cc7a5564d1b73fc"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set up the classifier with the current training features and labels <br /></td></tr>
<tr class="separator:a17909e1d3ca700241cc7a5564d1b73fc"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a572a1755d7e27dcaff6e15fed6387fc9"><td class="memItemLeft" align="right" valign="top"><a id="a572a1755d7e27dcaff6e15fed6387fc9"></a>
void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_v_f_h_classifier_n_n.html#a572a1755d7e27dcaff6e15fed6387fc9">finalizeTree</a> ()</td></tr>
<tr class="memdesc:a572a1755d7e27dcaff6e15fed6387fc9"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set up the classifier with the current training features <br /></td></tr>
<tr class="separator:a572a1755d7e27dcaff6e15fed6387fc9"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a97407c340fbfe7316b5e0ac091821128"><td class="memItemLeft" align="right" valign="top"><a id="a97407c340fbfe7316b5e0ac091821128"></a>
void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_v_f_h_classifier_n_n.html#a97407c340fbfe7316b5e0ac091821128">finalizeLabels</a> ()</td></tr>
<tr class="memdesc:a97407c340fbfe7316b5e0ac091821128"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set up the classifier with the current training example labels <br /></td></tr>
<tr class="separator:a97407c340fbfe7316b5e0ac091821128"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a16995bb93ff3408e34d6f4262558307f"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_v_f_h_classifier_n_n.html#a16995bb93ff3408e34d6f4262558307f">saveTrainingFeatures</a> (std::string file_name, std::string labels_file_name)</td></tr>
<tr class="memdesc:a16995bb93ff3408e34d6f4262558307f"><td class="mdescLeft">&#160;</td><td class="mdescRight">Save the list of training examples and corresponding labels.  <a href="classpcl_1_1_v_f_h_classifier_n_n.html#a16995bb93ff3408e34d6f4262558307f">更多...</a><br /></td></tr>
<tr class="separator:a16995bb93ff3408e34d6f4262558307f"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a99666d5d41670cb4f697d82b50438815"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_v_f_h_classifier_n_n.html#a99666d5d41670cb4f697d82b50438815">addTrainingFeatures</a> (const FeatureCloudPtr training_features, const std::vector&lt; std::string &gt; &amp;labels)</td></tr>
<tr class="memdesc:a99666d5d41670cb4f697d82b50438815"><td class="mdescLeft">&#160;</td><td class="mdescRight">Fill the list of training examples and corresponding labels.  <a href="classpcl_1_1_v_f_h_classifier_n_n.html#a99666d5d41670cb4f697d82b50438815">更多...</a><br /></td></tr>
<tr class="separator:a99666d5d41670cb4f697d82b50438815"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab0fc1dee96f2153683f20ca3d04bd8b7"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_v_f_h_classifier_n_n.html#ab0fc1dee96f2153683f20ca3d04bd8b7">loadTrainingFeatures</a> (std::string file_name, std::string labels_file_name)</td></tr>
<tr class="memdesc:ab0fc1dee96f2153683f20ca3d04bd8b7"><td class="mdescLeft">&#160;</td><td class="mdescRight">Fill the list of training examples and corresponding labels.  <a href="classpcl_1_1_v_f_h_classifier_n_n.html#ab0fc1dee96f2153683f20ca3d04bd8b7">更多...</a><br /></td></tr>
<tr class="separator:ab0fc1dee96f2153683f20ca3d04bd8b7"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab7890486e6a1a2fef2739b5584b76273"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_v_f_h_classifier_n_n.html#ab7890486e6a1a2fef2739b5584b76273">loadTrainingData</a> (std::string file_name, std::string label)</td></tr>
<tr class="memdesc:ab7890486e6a1a2fef2739b5584b76273"><td class="mdescLeft">&#160;</td><td class="mdescRight">Add the feature extracted from the cloud at the specified location as a training example with the given labels.  <a href="classpcl_1_1_v_f_h_classifier_n_n.html#ab7890486e6a1a2fef2739b5584b76273">更多...</a><br /></td></tr>
<tr class="separator:ab7890486e6a1a2fef2739b5584b76273"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa19d13823be5c2f50a3b7bec142fac00"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_v_f_h_classifier_n_n.html#aa19d13823be5c2f50a3b7bec142fac00">addTrainingData</a> (const <a class="el" href="structpcl_1_1_p_c_l_point_cloud2.html">pcl::PCLPointCloud2</a> &amp;training_data, std::string &amp;label)</td></tr>
<tr class="memdesc:aa19d13823be5c2f50a3b7bec142fac00"><td class="mdescLeft">&#160;</td><td class="mdescRight">Add the feature extracted from the cloud as a training example with the given labels.  <a href="classpcl_1_1_v_f_h_classifier_n_n.html#aa19d13823be5c2f50a3b7bec142fac00">更多...</a><br /></td></tr>
<tr class="separator:aa19d13823be5c2f50a3b7bec142fac00"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac1e03de3f4cd816900a90474f42f3749"><td class="memItemLeft" align="right" valign="top">ResultPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_v_f_h_classifier_n_n.html#ac1e03de3f4cd816900a90474f42f3749">classify</a> (const <a class="el" href="structpcl_1_1_p_c_l_point_cloud2.html">pcl::PCLPointCloud2</a> &amp;testing_data, double radius=300, double min_score=0.002)</td></tr>
<tr class="memdesc:ac1e03de3f4cd816900a90474f42f3749"><td class="mdescLeft">&#160;</td><td class="mdescRight">Utility function for the default classification process.  <a href="classpcl_1_1_v_f_h_classifier_n_n.html#ac1e03de3f4cd816900a90474f42f3749">更多...</a><br /></td></tr>
<tr class="separator:ac1e03de3f4cd816900a90474f42f3749"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a4f944f11daeb19da7779ac069f312b32"><td class="memItemLeft" align="right" valign="top">FeatureCloudPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_v_f_h_classifier_n_n.html#a4f944f11daeb19da7779ac069f312b32">computeFeature</a> (const <a class="el" href="structpcl_1_1_p_c_l_point_cloud2.html">pcl::PCLPointCloud2</a> &amp;points, double radius=0.03)</td></tr>
<tr class="memdesc:a4f944f11daeb19da7779ac069f312b32"><td class="mdescLeft">&#160;</td><td class="mdescRight">Extract the VFH feature describing the given point cloud.  <a href="classpcl_1_1_v_f_h_classifier_n_n.html#a4f944f11daeb19da7779ac069f312b32">更多...</a><br /></td></tr>
<tr class="separator:a4f944f11daeb19da7779ac069f312b32"><td class="memSeparator" colspan="2">&#160;</td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pri-attribs"></a>
Private 属性</h2></td></tr>
<tr class="memitem:aa3ca42a8830edebf8a2e51567d278259"><td class="memItemLeft" align="right" valign="top"><a id="aa3ca42a8830edebf8a2e51567d278259"></a>
FeatureCloudPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_v_f_h_classifier_n_n.html#aa3ca42a8830edebf8a2e51567d278259">training_features_</a></td></tr>
<tr class="memdesc:aa3ca42a8830edebf8a2e51567d278259"><td class="mdescLeft">&#160;</td><td class="mdescRight">Point cloud containing the training VFH features <br /></td></tr>
<tr class="separator:aa3ca42a8830edebf8a2e51567d278259"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:acc686059f7fe1a460ae1230ae40190f0"><td class="memItemLeft" align="right" valign="top"><a id="acc686059f7fe1a460ae1230ae40190f0"></a>
std::vector&lt; std::string &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_v_f_h_classifier_n_n.html#acc686059f7fe1a460ae1230ae40190f0">labels_</a></td></tr>
<tr class="memdesc:acc686059f7fe1a460ae1230ae40190f0"><td class="mdescLeft">&#160;</td><td class="mdescRight">Class label for each training example <br /></td></tr>
<tr class="separator:acc686059f7fe1a460ae1230ae40190f0"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3ed6c7ba5ed10b865a4f9d7ebce70867"><td class="memItemLeft" align="right" valign="top"><a id="a3ed6c7ba5ed10b865a4f9d7ebce70867"></a>
<a class="el" href="classpcl_1_1_n_n_classification.html">NNClassification</a>&lt; <a class="el" href="structpcl_1_1_v_f_h_signature308.html">pcl::VFHSignature308</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_v_f_h_classifier_n_n.html#a3ed6c7ba5ed10b865a4f9d7ebce70867">classifier_</a></td></tr>
<tr class="memdesc:a3ed6c7ba5ed10b865a4f9d7ebce70867"><td class="mdescLeft">&#160;</td><td class="mdescRight">Nearest neighbor classifier instantiated for VFH features <br /></td></tr>
<tr class="separator:a3ed6c7ba5ed10b865a4f9d7ebce70867"><td class="memSeparator" colspan="2">&#160;</td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">详细描述</h2>
<div class="textblock"><p>Utility class for nearest neighbor search based classification of VFH features. </p>
<dl class="section author"><dt>作者</dt><dd>Zoltan Csaba Marton </dd></dl>
</div><h2 class="groupheader">成员函数说明</h2>
<a id="aa19d13823be5c2f50a3b7bec142fac00"></a>
<h2 class="memtitle"><span class="permalink"><a href="#aa19d13823be5c2f50a3b7bec142fac00">&#9670;&nbsp;</a></span>addTrainingData()</h2>

<div class="memitem">
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          <td class="memname">bool pcl::VFHClassifierNN::addTrainingData </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="structpcl_1_1_p_c_l_point_cloud2.html">pcl::PCLPointCloud2</a> &amp;&#160;</td>
          <td class="paramname"><em>training_data</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::string &amp;&#160;</td>
          <td class="paramname"><em>label</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
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<p>Add the feature extracted from the cloud as a training example with the given labels. </p>
<dl class="section note"><dt>注解</dt><dd>this function has a cumulative effect. </dd></dl>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">training_data</td><td>point cloud for training feature extraction </td></tr>
    <tr><td class="paramname">label</td><td>the class label for the training example </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>true on success, false on failure (read error or number of entries don't match) </dd></dl>
<div class="fragment"><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;      {</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;        <span class="comment">// Create label list containing the single label</span></div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;        std::vector&lt;std::string&gt; labels;</div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;        labels.push_back (label);</div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160; </div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;        <span class="comment">// Compute the feature from the cloud and add it as a training example</span></div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;        FeatureCloudPtr vfhs = <a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#a4f944f11daeb19da7779ac069f312b32">computeFeature</a> (training_data);</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;        <span class="keywordflow">return</span> <a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#a99666d5d41670cb4f697d82b50438815">addTrainingFeatures</a>(vfhs, labels);</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_classifier_n_n_html_a4f944f11daeb19da7779ac069f312b32"><div class="ttname"><a href="classpcl_1_1_v_f_h_classifier_n_n.html#a4f944f11daeb19da7779ac069f312b32">pcl::VFHClassifierNN::computeFeature</a></div><div class="ttdeci">FeatureCloudPtr computeFeature(const pcl::PCLPointCloud2 &amp;points, double radius=0.03)</div><div class="ttdoc">Extract the VFH feature describing the given point cloud.</div><div class="ttdef"><b>Definition:</b> vfh_nn_classifier.h:260</div></div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_classifier_n_n_html_a99666d5d41670cb4f697d82b50438815"><div class="ttname"><a href="classpcl_1_1_v_f_h_classifier_n_n.html#a99666d5d41670cb4f697d82b50438815">pcl::VFHClassifierNN::addTrainingFeatures</a></div><div class="ttdeci">bool addTrainingFeatures(const FeatureCloudPtr training_features, const std::vector&lt; std::string &gt; &amp;labels)</div><div class="ttdoc">Fill the list of training examples and corresponding labels.</div><div class="ttdef"><b>Definition:</b> vfh_nn_classifier.h:170</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a99666d5d41670cb4f697d82b50438815">&#9670;&nbsp;</a></span>addTrainingFeatures()</h2>

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          <td class="memname">bool pcl::VFHClassifierNN::addTrainingFeatures </td>
          <td>(</td>
          <td class="paramtype">const FeatureCloudPtr&#160;</td>
          <td class="paramname"><em>training_features</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const std::vector&lt; std::string &gt; &amp;&#160;</td>
          <td class="paramname"><em>labels</em>&#160;</td>
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<p>Fill the list of training examples and corresponding labels. </p>
<dl class="section note"><dt>注解</dt><dd>this function has a cumulative effect. </dd></dl>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">training_features</td><td>the training features </td></tr>
    <tr><td class="paramname">labels</td><td>the class label for each training example </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>true on success, false on failure (number of entries don't match) </dd></dl>
<div class="fragment"><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;      {</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;        <span class="keywordflow">if</span> (labels.size () == training_features-&gt;points.size ())</div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;        {</div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;          <a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#acc686059f7fe1a460ae1230ae40190f0">labels_</a>.insert (<a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#acc686059f7fe1a460ae1230ae40190f0">labels_</a>.end (), labels.begin (), labels.end ());</div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;          <a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#aa3ca42a8830edebf8a2e51567d278259">training_features_</a>-&gt;points.insert (<a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#aa3ca42a8830edebf8a2e51567d278259">training_features_</a>-&gt;points.end (), training_features-&gt;points.begin (), training_features-&gt;points.end ());</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;          <a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#aa3ca42a8830edebf8a2e51567d278259">training_features_</a>-&gt;header = training_features-&gt;header;</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;          <a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#aa3ca42a8830edebf8a2e51567d278259">training_features_</a>-&gt;height = 1;</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;          <a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#aa3ca42a8830edebf8a2e51567d278259">training_features_</a>-&gt;width  = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span> (<a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#aa3ca42a8830edebf8a2e51567d278259">training_features_</a>-&gt;points.size ());</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;          <a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#aa3ca42a8830edebf8a2e51567d278259">training_features_</a>-&gt;is_dense &amp;= training_features-&gt;is_dense;</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;          <a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#aa3ca42a8830edebf8a2e51567d278259">training_features_</a>-&gt;sensor_origin_ = training_features-&gt;sensor_origin_;</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;          <a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#aa3ca42a8830edebf8a2e51567d278259">training_features_</a>-&gt;sensor_orientation_ = training_features-&gt;sensor_orientation_;</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;          <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;        }</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;        <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_classifier_n_n_html_aa3ca42a8830edebf8a2e51567d278259"><div class="ttname"><a href="classpcl_1_1_v_f_h_classifier_n_n.html#aa3ca42a8830edebf8a2e51567d278259">pcl::VFHClassifierNN::training_features_</a></div><div class="ttdeci">FeatureCloudPtr training_features_</div><div class="ttdoc">Point cloud containing the training VFH features</div><div class="ttdef"><b>Definition:</b> vfh_nn_classifier.h:106</div></div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_classifier_n_n_html_acc686059f7fe1a460ae1230ae40190f0"><div class="ttname"><a href="classpcl_1_1_v_f_h_classifier_n_n.html#acc686059f7fe1a460ae1230ae40190f0">pcl::VFHClassifierNN::labels_</a></div><div class="ttdeci">std::vector&lt; std::string &gt; labels_</div><div class="ttdoc">Class label for each training example</div><div class="ttdef"><b>Definition:</b> vfh_nn_classifier.h:108</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#ac1e03de3f4cd816900a90474f42f3749">&#9670;&nbsp;</a></span>classify()</h2>

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          <td>(</td>
          <td class="paramtype">const <a class="el" href="structpcl_1_1_p_c_l_point_cloud2.html">pcl::PCLPointCloud2</a> &amp;&#160;</td>
          <td class="paramname"><em>testing_data</em>, </td>
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          <td></td>
          <td class="paramtype">double&#160;</td>
          <td class="paramname"><em>radius</em> = <code>300</code>, </td>
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          <td></td>
          <td class="paramtype">double&#160;</td>
          <td class="paramname"><em>min_score</em> = <code>0.002</code>&#160;</td>
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<p>Utility function for the default classification process. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">testing_data</td><td>the point clouds to be classified </td></tr>
    <tr><td class="paramname">radius</td><td>the maximum search radius in feature space &ndash; 300 by default </td></tr>
    <tr><td class="paramname">minimum_score</td><td>the score to be given to matches at maximum distance (&gt;0) &ndash; 0.002 by default </td></tr>
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<dl class="section return"><dt>返回</dt><dd>pair of label and score for each relevant training class </dd></dl>
<div class="fragment"><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;      {</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;        <span class="comment">// compute the VFH feature for this point cloud</span></div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;        FeatureCloudPtr vfhs = <a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#a4f944f11daeb19da7779ac069f312b32">computeFeature</a> (testing_data);</div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;        <span class="comment">// compute gaussian parameter producing the desired minimum score (around 50 for the default values)</span></div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;        <span class="keywordtype">float</span> gaussian_param = - <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (radius / log (min_score));</div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;        <span class="comment">// TODO accept result to be filled in by reference</span></div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;        <span class="keywordflow">return</span> <a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#a3ed6c7ba5ed10b865a4f9d7ebce70867">classifier_</a>.<a class="code" href="classpcl_1_1_n_n_classification.html#ad0a5ee6bfd2250845459c03bd542fa74">classify</a>(vfhs-&gt;points.at (0), radius, gaussian_param);</div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_n_n_classification_html_ad0a5ee6bfd2250845459c03bd542fa74"><div class="ttname"><a href="classpcl_1_1_n_n_classification.html#ad0a5ee6bfd2250845459c03bd542fa74">pcl::NNClassification::classify</a></div><div class="ttdeci">ResultPtr classify(const PointT &amp;p_q, double radius, float gaussian_param, int max_nn=INT_MAX)</div><div class="ttdoc">Utility function for the default classification process.</div><div class="ttdef"><b>Definition:</b> nn_classification.h:185</div></div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_classifier_n_n_html_a3ed6c7ba5ed10b865a4f9d7ebce70867"><div class="ttname"><a href="classpcl_1_1_v_f_h_classifier_n_n.html#a3ed6c7ba5ed10b865a4f9d7ebce70867">pcl::VFHClassifierNN::classifier_</a></div><div class="ttdeci">NNClassification&lt; pcl::VFHSignature308 &gt; classifier_</div><div class="ttdoc">Nearest neighbor classifier instantiated for VFH features</div><div class="ttdef"><b>Definition:</b> vfh_nn_classifier.h:110</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a4f944f11daeb19da7779ac069f312b32">&#9670;&nbsp;</a></span>computeFeature()</h2>

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          <td>(</td>
          <td class="paramtype">const <a class="el" href="structpcl_1_1_p_c_l_point_cloud2.html">pcl::PCLPointCloud2</a> &amp;&#160;</td>
          <td class="paramname"><em>points</em>, </td>
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          <td class="paramtype">double&#160;</td>
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<p>Extract the VFH feature describing the given point cloud. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">points</td><td>point cloud for feature extraction </td></tr>
    <tr><td class="paramname">radius</td><td>search radius for normal estimation &ndash; 0.03 m by default </td></tr>
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  </dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>point cloud containing the extracted feature </dd></dl>
<div class="fragment"><div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;      {</div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;        pcl::PointCloud&lt;pcl::PointXYZ&gt;::Ptr cloud (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::PointXYZ&gt;</a> ());</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;        pcl::fromPCLPointCloud2 (points, *cloud);</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;        <span class="keywordflow">return</span> pcl::computeVFH&lt;pcl::PointXYZ&gt; (cloud, radius);</div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html"><div class="ttname"><a href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt; pcl::PointXYZ &gt;</a></div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#ab7890486e6a1a2fef2739b5584b76273">&#9670;&nbsp;</a></span>loadTrainingData()</h2>

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          <td>(</td>
          <td class="paramtype">std::string&#160;</td>
          <td class="paramname"><em>file_name</em>, </td>
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          <td class="paramtype">std::string&#160;</td>
          <td class="paramname"><em>label</em>&#160;</td>
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<p>Add the feature extracted from the cloud at the specified location as a training example with the given labels. </p>
<dl class="section note"><dt>注解</dt><dd>this function has a cumulative effect. </dd></dl>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">file_name</td><td>PCD file containing the training data </td></tr>
    <tr><td class="paramname">label</td><td>the class label for the training example </td></tr>
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<dl class="section return"><dt>返回</dt><dd>true on success, false on failure (read error or number of entries don't match) </dd></dl>
<div class="fragment"><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;      {</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;        <a class="code" href="structpcl_1_1_p_c_l_point_cloud2.html">pcl::PCLPointCloud2</a> cloud_blob;</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;        <span class="keywordflow">if</span> (pcl::io::loadPCDFile (file_name.c_str (), cloud_blob) != 0)</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;          <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;        <span class="keywordflow">return</span> <a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#aa19d13823be5c2f50a3b7bec142fac00">addTrainingData</a> (cloud_blob, label);</div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_classifier_n_n_html_aa19d13823be5c2f50a3b7bec142fac00"><div class="ttname"><a href="classpcl_1_1_v_f_h_classifier_n_n.html#aa19d13823be5c2f50a3b7bec142fac00">pcl::VFHClassifierNN::addTrainingData</a></div><div class="ttdeci">bool addTrainingData(const pcl::PCLPointCloud2 &amp;training_data, std::string &amp;label)</div><div class="ttdoc">Add the feature extracted from the cloud as a training example with the given labels.</div><div class="ttdef"><b>Definition:</b> vfh_nn_classifier.h:228</div></div>
<div class="ttc" id="astructpcl_1_1_p_c_l_point_cloud2_html"><div class="ttname"><a href="structpcl_1_1_p_c_l_point_cloud2.html">pcl::PCLPointCloud2</a></div><div class="ttdef"><b>Definition:</b> PCLPointCloud2.h:21</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#ab0fc1dee96f2153683f20ca3d04bd8b7">&#9670;&nbsp;</a></span>loadTrainingFeatures()</h2>

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          <td class="paramname"><em>file_name</em>, </td>
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          <td class="paramname"><em>labels_file_name</em>&#160;</td>
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<p>Fill the list of training examples and corresponding labels. </p>
<dl class="section note"><dt>注解</dt><dd>this function has a cumulative effect. </dd></dl>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">file_name</td><td>PCD file containing the training features </td></tr>
    <tr><td class="paramname">labels_file_name</td><td>the class label for each training example </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>true on success, false on failure (read error or number of entries don't match) </dd></dl>
<div class="fragment"><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;      {</div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;        FeatureCloudPtr cloud (<span class="keyword">new</span> FeatureCloud);</div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;        <span class="keywordflow">if</span> (pcl::io::loadPCDFile (file_name.c_str (), *cloud) != 0)</div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;          <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;        std::vector&lt;std::string&gt; labels;</div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;        std::ifstream f (labels_file_name.c_str ());</div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;        std::string label;</div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;        <span class="keywordflow">while</span> (getline (f, label))</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;          <span class="keywordflow">if</span> (label.size () &gt; 0)</div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;            labels.push_back(label);</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;        <span class="keywordflow">return</span> <a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#a99666d5d41670cb4f697d82b50438815">addTrainingFeatures</a> (cloud, labels);</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a16995bb93ff3408e34d6f4262558307f">&#9670;&nbsp;</a></span>saveTrainingFeatures()</h2>

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          <td class="memname">bool pcl::VFHClassifierNN::saveTrainingFeatures </td>
          <td>(</td>
          <td class="paramtype">std::string&#160;</td>
          <td class="paramname"><em>file_name</em>, </td>
        </tr>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::string&#160;</td>
          <td class="paramname"><em>labels_file_name</em>&#160;</td>
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          <td></td>
          <td>)</td>
          <td></td><td></td>
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<p>Save the list of training examples and corresponding labels. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">file_name</td><td>file name for writing the training features </td></tr>
    <tr><td class="paramname">labels_file_name</td><td>file name for writing the class label for each training example </td></tr>
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  </dd>
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<dl class="section return"><dt>返回</dt><dd>true on success, false on failure (write error or number of entries don't match) </dd></dl>
<div class="fragment"><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;      {</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;        <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#acc686059f7fe1a460ae1230ae40190f0">labels_</a>.size () == <a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#aa3ca42a8830edebf8a2e51567d278259">training_features_</a>-&gt;points.size ())</div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;        {</div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;          <span class="keywordflow">if</span> (pcl::io::savePCDFile (file_name.c_str (), *<a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#aa3ca42a8830edebf8a2e51567d278259">training_features_</a>) != 0)</div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;            <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;          std::ofstream f (labels_file_name.c_str ());</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;          BOOST_FOREACH (std::string s, <a class="code" href="classpcl_1_1_v_f_h_classifier_n_n.html#acc686059f7fe1a460ae1230ae40190f0">labels_</a>)</div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;            f &lt;&lt; s &lt;&lt; <span class="stringliteral">&quot;\n&quot;</span>;</div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;          <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;        }</div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;        <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;      }</div>
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<hr/>该类的文档由以下文件生成:<ul>
<li>apps/include/pcl/apps/<a class="el" href="vfh__nn__classifier_8h_source.html">vfh_nn_classifier.h</a></li>
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